Loading extensions-builtin/Lora/network.py +3 −3 Original line number Diff line number Diff line Loading @@ -145,10 +145,10 @@ class NetworkModule: if orig_weight.size().numel() == updown.size().numel(): updown = updown.reshape(orig_weight.shape) if ex_bias is None: ex_bias = 0 if ex_bias is not None: ex_bias = ex_bias * self.multiplier() return updown * self.calc_scale() * self.multiplier(), ex_bias * self.multiplier() return updown * self.calc_scale() * self.multiplier(), ex_bias def calc_updown(self, target): raise NotImplementedError() Loading extensions-builtin/Lora/networks.py +1 −1 Original line number Diff line number Diff line Loading @@ -322,7 +322,7 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn updown = torch.nn.functional.pad(updown, (0, 0, 0, 0, 0, 5)) self.weight += updown if getattr(self, 'bias', None) is not None: if ex_bias is not None and getattr(self, 'bias', None) is not None: self.bias += ex_bias continue Loading Loading
extensions-builtin/Lora/network.py +3 −3 Original line number Diff line number Diff line Loading @@ -145,10 +145,10 @@ class NetworkModule: if orig_weight.size().numel() == updown.size().numel(): updown = updown.reshape(orig_weight.shape) if ex_bias is None: ex_bias = 0 if ex_bias is not None: ex_bias = ex_bias * self.multiplier() return updown * self.calc_scale() * self.multiplier(), ex_bias * self.multiplier() return updown * self.calc_scale() * self.multiplier(), ex_bias def calc_updown(self, target): raise NotImplementedError() Loading
extensions-builtin/Lora/networks.py +1 −1 Original line number Diff line number Diff line Loading @@ -322,7 +322,7 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn updown = torch.nn.functional.pad(updown, (0, 0, 0, 0, 0, 5)) self.weight += updown if getattr(self, 'bias', None) is not None: if ex_bias is not None and getattr(self, 'bias', None) is not None: self.bias += ex_bias continue Loading
